Upper extremity musculoskeletal injuries are common in hand intensive work involving highly repetitive motions and exertions. Previous exposure assessment methods involve either direct measurements using instruments attached to a worker's hands or arms, or indirect observations. Both methods however are highly impractical for occupational health practice. Compared to instruments, indirect observation lacks precision and accuracy, is not suitable for long observation periods, and requires considerable analyst time. Alternatively, attaching sensors on working hands is too time consuming, and sensors may interfere with normal working operation. We are developing novel real- time video processing algorithms to automatically and unobtrusively measure upper limb kinematics from conventional single camera digital video. In this exploratory research, we propose using a video feature extraction method to indirectly quantify hand activity using markerless video motion analysis. We are collaborating with the NIOSH Industry-wide Studies Branch, which is making more than 700 videos of 484 workers at three study locations, their associated observational analyses and longitudinal health outcome data available to us for this initial study. The real time video algorithms will first be developed and designed to be robust using sequential Bayesian estimation to track the motion of a general region of interest (ROI) on the upper extremities selected by the camera operator or analyst, and statistically estimating its velocity and acceleration. Video derived ROI kinematics from varying vantage points will then be compared against ground truth measurements obtained using a 3D infrared motion capture system, and conventional observational measurements of laboratory participants performing controlled repetitive motion tasks. We will also perform secondary analysis of videos from actual workplace activities taken and already studied by NIOSH, for algorithm refinement and for studying the correlation between the kinematic data and conventional observational exposure measures. We will evaluate the risk of an injury from the NIOSH health outcome data using our new exposure analysis method and compare the resulting dose-response model using the measured frequency, duty cycle, velocity and acceleration from our automatic video analysis method against conventional manual analysis in order to evaluate improvement over existing methods. This exploratory research could ultimately lead to a full-scale epidemiology and validation study of our video exposure assessment methodology that would leverage the existing NIOSH consortium database from seven participating laboratories in order to establish a model relating video derived kinematics and health outcome. The long-term goal is to develop a video-based direct reading exposure assessment instrument for upper limb repetitive motion activities that should be useful for evaluating the risk of injuries in the workplace, and for primry prevention.